Instructions to use rorito/perfectfullround with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rorito/perfectfullround with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("rorito/perfectfullround") prompt = "UNICODE\u0000\u00008\u0000K\u0000,\u0000 \u0000d\u0000e\u0000p\u0000t\u0000h\u0000 \u0000o\u0000f\u0000 \u0000f\u0000i\u0000e\u0000l\u0000d\u0000,\u0000 \u0000f\u0000o\u0000c\u0000u\u0000s\u0000e\u0000d\u0000 \u0000s\u0000u\u0000b\u0000j\u0000e\u0000c\u0000t\u0000,\u0000 \u0000d\u0000y\u0000n\u0000a\u0000m\u0000i\u0000c\u0000 \u0000a\u0000n\u0000g\u0000l\u0000e\u0000,\u0000 \u0000b\u0000e\u0000s\u0000t\u0000 \u0000q\u0000u\u0000a\u0000l\u0000i\u0000t\u0000y\u0000,\u0000 \u0000a\u0000 \u0000b\u0000e\u0000a\u0000u\u0000t\u0000i\u0000f\u0000u\u0000l\u0000 \u0000w\u0000o\u0000m\u0000a\u0000n\u0000,\u0000 \u0000a\u0000s\u0000y\u0000m\u0000m\u0000e\u0000t\u0000r\u0000i\u0000c\u0000 \u0000h\u0000a\u0000i\u0000r\u0000,\u0000 \u0000t\u0000a\u0000n\u0000n\u0000e\u0000d\u0000 \u0000s\u0000k\u0000i\u0000n\u0000,\u0000 \u0000d\u0000i\u0000r\u0000t\u0000y\u0000 \u0000s\u0000k\u0000i\u0000n\u0000,\u0000 \u0000s\u0000w\u0000e\u0000a\u0000t\u0000,\u0000 \u0000t\u0000a\u0000t\u0000t\u0000o\u0000o\u0000s\u0000,\u0000 \u0000p\u0000i\u0000e\u0000r\u0000c\u0000i\u0000n\u0000g\u0000s\u0000,\u0000 \u0000p\u0000i\u0000e\u0000r\u0000c\u0000e\u0000d\u0000 \u0000n\u0000i\u0000p\u0000p\u0000l\u0000e\u0000s\u0000,\u0000 \u0000p\u0000i\u0000e\u0000r\u0000c\u0000e\u0000d\u0000 \u0000c\u0000l\u0000i\u0000t\u0000,\u0000 \u0000d\u0000i\u0000r\u0000t\u0000y\u0000 \u0000c\u0000l\u0000o\u0000t\u0000h\u0000e\u0000s\u0000,\u0000 \u0000s\u0000t\u0000u\u0000d\u0000d\u0000e\u0000d\u0000 \u0000l\u0000e\u0000a\u0000t\u0000h\u0000e\u0000r\u0000 \u0000j\u0000a\u0000c\u0000k\u0000e\u0000t\u0000,\u0000 \u0000r\u0000i\u0000p\u0000p\u0000e\u0000d\u0000 \u0000c\u0000l\u0000o\u0000t\u0000h\u0000e\u0000s\u0000,\u0000 \u0000l\u0000e\u0000a\u0000t\u0000h\u0000e\u0000r\u0000 \u0000s\u0000t\u0000r\u0000a\u0000p\u0000s\u0000,\u0000 \u0000p\u0000o\u0000s\u0000t\u0000 \u0000a\u0000p\u0000o\u0000c\u0000a\u0000l\u0000y\u0000p\u0000t\u0000i\u0000c\u0000 \u0000s\u0000t\u0000y\u0000l\u0000e\u0000,\u0000 \u0000p\u0000e\u0000r\u0000f\u0000e\u0000c\u0000t\u0000 \u0000e\u0000y\u0000e\u0000s\u0000,\u0000 \u0000l\u0000o\u0000o\u0000k\u0000i\u0000n\u0000g\u0000 \u0000a\u0000t\u0000 \u0000v\u0000i\u0000e\u0000w\u0000e\u0000r\u0000,\u0000 \u0000c\u0000h\u0000a\u0000i\u0000n\u0000s\u0000,\u0000 \u0000b\u0000l\u0000a\u0000c\u0000k\u0000 \u0000b\u0000o\u0000o\u0000t\u0000s\u0000,\u0000 \u0000c\u0000u\u0000r\u0000v\u0000y\u0000 \u0000b\u0000o\u0000d\u0000y\u0000,\u0000 \u0000m\u0000o\u0000v\u0000i\u0000e\u0000 \u0000p\u0000e\u0000r\u0000s\u0000p\u0000e\u0000c\u0000t\u0000i\u0000v\u0000e\u0000,\u0000 \u0000w\u0000a\u0000s\u0000t\u0000e\u0000l\u0000a\u0000n\u0000d\u0000,\u0000 \u0000d\u0000u\u0000s\u0000t\u0000,\u0000 \u0000E\u0000v\u0000 \u0000G\u0000a\u0000n\u0000i\u0000n\u0000 \u0000s\u0000t\u0000y\u0000l\u0000e\u0000" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
perfectfullround

- Prompt
- UNICODE8K, depth of field, focused subject, dynamic angle, best quality, a beautiful woman, asymmetric hair, tanned skin, dirty skin, sweat, tattoos, piercings, pierced nipples, pierced clit, dirty clothes, studded leather jacket, ripped clothes, leather straps, post apocalyptic style, perfect eyes, looking at viewer, chains, black boots, curvy body, movie perspective, wasteland, dust, Ev Ganin style
Model description
work
Trigger words
You should use woman to trigger the image generation.
Download model
Download them in the Files & versions tab.
- Downloads last month
- 2